Annotation and curation protocols for making sense of decentralized science.
While Decentralized Science (DeSci) is a promising approach towards effectively broadening access to scientific research, the problem of making sense of the volumes of new information being published in DeSci and traditional science remains unsolved.
Traditional curation methods like peer-reviewed journals are failing to keep pace, resulting in unprecedented information overload and knowledge fragmentation. We contend that making sense of DeSci research requires open access to diverse sources of scholarly sensemaking data. Sensemaking data are the digital traces of sensemaking processes, including explicit annotations (tags, votes, ratings) and commentary made by researchers, as well as implicit behavioral data generated through app usage (reference managers, etc). The problem is that these sensemaking traces are currently scattered and siloed across a multitude of apps and formats and increasingly enclosed by platforms for profit (e.g. Twitter). We propose Open Source Sensemaking, an interoperable and decentralized annotation network, enabling researchers to record, own and share their sensemaking data, thus contributing to the network while remaining resilient to platform capture. The shared sensemaking data will greatly benefit individual and collective sensemaking by enabling the development of diverse discovery services, from simple aggregation of reviews and ratings (e.g., “Goodreads” for scientific research) to more advanced AI-augmented scientific intelligence systems.
Open and accessible sensemaking data and tools are instrumental for making meaningful progress on multiple key challenges for DeSci, including:
These challenges are typically viewed separately, and we believe that illuminating a key root cause underlying all of these issues (fragmented sensemaking data) can help direct attention and efforts toward developing more effective solutions.